AI is a force both hailed and feared by the leading tech voices of this generation. And while it does pose certain challenges to traditional cybersecurity concerns (what’s more concerning than a machine that can think and act for itself?), AI is being considered a force, pivotal, to the changing cybersecurity landscape.
Machine learning, in fact, is likely to play a significant role in this process, alongside AI’s natural language capabilities. Is this Greek to you? In our post, we examine the movement that’s AI for cybersecurity and how this is set to affect the security environment and the cybersecurity industry.
WHAT ARE THE INDUSTRY-LEVEL CHANGES AI WILL BE RESPONSIBLE FOR?
At present, legacy cybersecurity systems rely on the manual effort of human agents to provide the often meagre protection that they do. Firewall policies, backup schedules, incident response, and so much more need some level of human input.
With AI for cybersecurity, Artifical Intelligence-driven and smart tools will be able to dive into specific aspects of cybersecurity efforts including event monitoring and incident response. Even firewalls will experience a futuristic makeover and host machine learning capabilities, allowing its software to recognise patterns in web requests and other data to predict and effectively prevent and mitigate attacks.
AI’s natural language capabilities are also expected to play a leading role. It is believed that by scanning and analysing large sets of data on the internet, AI systems will be able to understand how cybersecurity threats take form and may be able to suggest solutions to prevent these.
HOW IS AI BEING USED AT PRESENT?
While AI for cybersecurity is still being refined and will take some time to reach its full powers, nascent technology has allowed certain companies to refine their cybersecurity efforts.
For instance, Gmail leverages machine learning to filter emails and protect your inbox from the influx of malicious emails. IBM and Balbix have both honed the power of machine learning to detect threats, reduce risk, and thereby prevent data and security breaches in real-time.
Using data mining, machine learning algorithms, and security domain learnings, Microsoft is protecting the vast quantity of data being generated by its systems and services. Using these AI tools, data is being analysed for the purposes of anomaly detection and to uncover malicious network traffic.
WHAT ARE THE CHALLENGES TO THIS TYPE OF APPROACH?
It would seem obvious, certainly inevitable, that as cybersecurity practices and tools grow more sophisticated through the use of AI, so it will be for the attacks and threats that plague security systems.
Advanced hacking programmes that imitate AI-based algorithms are likely to become serious risks going forward. This will force human agents to actively enter the fray, once more, and support AI-led cybersecurity efforts. Any system-level decision-making that’s being automated will need to be carefully planned and analysed.
Machine learning, in particular, requires feedback to determine what’s threatening or risky and what’s not. In the absence of this, malicious attacks can be designed to appear non-threatening and slip past AI’s protective barriers. Within the industry, this is known as ‘adversarial machine learning.
In short, both AI-based protective mechanisms and risks will advance, forcing human agents to supplement AI with other cybersecurity-enhancing procedures, policies, and systems.
AI FOR CYBERSECURITY WILL TRANSFORM TRADITIONAL, HUMAN-LED PROCESSES
As we move towards a future where AI slowly eclipses manual efforts for greater cybersecurity protection, staying up-to-date with industry trends and recommendations is crucial for comprehensive coverage and security.
AI is an incoming tide that’s impossible to reverse. Refusal to embrace it will only incur a greater risk for businesses. It is important to remember, however, that AI for cybersecurity is not a panacea. It needs to be honed with plenty of care and consideration and may even necessitate the addition of protective layers, especially against hacking that’s based on AI algorithms.